具身智能-强化学习(灵巧操作方向) 实习生
Tasks
- Design and train reward models
- Design reinforcement learning training plans on real robots
- Evaluate training results with benchmarks
- Handle real world delays noise and hardware nonlinearities
- Improve generalization and robustness in unstructured environments
- Integrate foundation models with reinforcement learning
- Iterate using real robot data
- Perform supervised fine tuning and reinforcement learning
- Track and review cutting edge embodied intelligence papers
Perks/Benefits
- N/A
Skills/Tech-stack
Actor-critic | Diffusion Models | Distributed Training | Embodied intelligence | Flow matching | Multimodal Learning | Offline Reinforcement Learning | Online Reinforcement Learning | Policy Optimization | Proximal Policy Optimization | PyTorch | Python | Reinforcement Learning | Robot Learning | Robotics Kinematics | Robotics dynamics | Soft Actor Critic
Education
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